Taxonomy based targeted search advertising

ABSTRACT

Campaign creation systems and methods for building taxonomy based targeted search advertising campaigns in accordance with embodiments of the invention are disclosed. One embodiment of the invention includes obtaining source data describing a plurality of landing pages, obtaining a list of keywords using the campaign creation server system, where the list of keywords comprises keyword component, building a taxonomy using the source data and the initial list of keywords, where the taxonomy uniquely maps the plurality of landing pages to categories and attributes and maps the keyword components to the categories and attributes, mapping keywords to relevant offers based on the taxonomy, automatically generating adgroups based on the offers and relevant keywords, where each adgroup includes a landing page, at least one creative and at least one keyword, and deploying the adgroups to a search engine provider.

CROSS-REFERENCE TO RELATED APPLICATION

The present invention is a continuation of U.S. application Ser. No.13/424,373 filed Mar. 19, 2012 which application claimed the benefitunder 35 U.S.C. §119(e) of the filing date of U.S. ProvisionalApplication Ser. No. 61/453,954 entitled “Taxonomy Based Targeted SearchAdvertising” to Zimmerman et al. filed Mar. 17, 2011, the disclosure ofwhich is incorporated by reference herein in its entirety.

FIELD OF THE INVENTION

The present invention relates to targeted search advertising and morespecifically to the use of a taxonomy to build a targeted searchadvertising campaign.

BACKGROUND

The term e-commerce is used to refer to the buying and selling ofproducts or services over electronic systems such as the Internet andother computer networks. The amount of trade conducted via e-commercehas grown extraordinarily with widespread Internet usage. As a result, avariety of websites have been established to offer goods and services. Acommon configuration for such a website is to present each individualgood or service on a separate landing page. Such websites also typicallyorganize the goods and/or services offered in a hierarchy and featurecategories of goods and/or services on separate landing pages.

Search engines are useful tools for locating specific pages ofinformation on the World Wide Web and are increasingly used to locategoods and services. As a result, many websites use searchadvertising/search engine marketing to attract visitors to product,service and/or category landing pages. Search advertising describes theplacement of online advertisements adjacent or amongst the searchresults returned by a search engine in response to a specific searchquery. Search engine marketing typically involves paying for a specificonline advertisement or creative to be featured in or adjacent to thesearch results provided in response to a specific query. The creativetypically includes a link to a specific landing page. In many instances,the provider of the search engine derives revenue when a user of thesearch engine uses the link within the creative to navigate to thelanding page. In addition, the amount of money paid is often the resultof a bidding process. Typically, the position of an advertisement withinthe returned search results is a function of the bid scaled by a qualityfactor that measures the relevance of the creative and landing pagecombination to the search query. Accordingly, the provider of the searchengine is incentivized to feature relevant keyword/advertisement/landingpage combinations so that users will select featured advertisements andincrease the revenue generated by the search engine provider. In thecontext of paid search advertising, the term keyword refers to both asingle word and a specific combination of words or keyword components.

When a website includes a large number of products or services, theprocess of building and managing a paid search advertising campaign canbe quite complex. Many search engines provide the ability to upload anentire campaign including one or more creatives that target a set ofkeywords, and associated bids to be used when the display of thecreative is triggered by specific keywords. For example, Google, Inc. ofMountain View, Calif., defines an Ad Group file format that enablesadvertisers to upload paid search advertising campaigns.

Leading search engine providers offer a variety of different match typeswhen building a paid search advertising campaign. Keyword matchingoptions or match types specify the similarity required between a keywordin a search query and a keyword specified in a paid search advertisingcampaign in order to trigger the display of a creative. Google, Inc.provides four different match types. “Broad match” matches keywords tosimilar phrases and relevant variations in a search query. “ModifiedBroad Match” is similar to “broad match”, but with the restriction thatat least one specified keyword component or a close variant must matchexactly within the search query. “Phrase match” requires that thekeyword be present within the query exactly. “Exact match” requires thatthe keyword and the search query match exactly. In addition to the fourmatch types, “negative matches” can also be specified to prevent acreative from being displayed when a specific keyword is present.

SUMMARY OF THE INVENTION

Campaign creation systems and methods for building taxonomy basedtargeted search advertising campaigns in accordance with embodiments ofthe invention are disclosed. One embodiment of the invention includesobtaining source data describing a plurality of landing pages using acampaign creation server system, obtaining a list of initial keywordsusing the campaign creation server system, where the keywords in theinitial list of keywords comprise keyword component, building a taxonomybased on the source data and the initial list of keywords using thecampaign creation server system, where the taxonomy uniquely maps theplurality of landing pages to categories and attributes and maps thekeyword components to the categories and attributes, mapping keywords torelevant offers based on the taxonomy using the campaign creation serversystem, automatically generating adgroups based on the offers andrelevant keywords using the campaign creation server system, where eachadgroup includes a landing page, at least one creative and at least onekeyword, an deploying the adgroups to a search engine provider using thecampaign creation server system.

A further embodiment also includes generating the at least one creativebased upon the attributes of an offer using the campaign creation serversystem, and including the at least one creative in the adgroupassociated with the offer using the campaign creation server system.

Another embodiment of the invention also include identifying a landingpage based upon the attributes of an offer using the campaign creationserver system, and including the landing page in the adgroup associatedwith the offer using the campaign creation server system.

A still further embodiment also includes generating source data bycrawling the plurality of landing pages to identify pieces ofinformation within the landing pages selected from the group consistingof information concerning products and information concerning servicesusing the campaign creation server system.

Still another embodiment also includes generating source data bycrawling landing pages within the plurality of landing pages thatcontain category information using the campaign creation server system.

In a yet further embodiment, building a taxonomy that maps keywords tospecific landing pages within the plurality of landing pages comprisesdefining categories within the taxonomy based on the categoryinformation obtained by crawling the landing pages that contain categoryinformation using the campaign creation server system.

Yet another embodiment also includes obtaining source data using thesearch results pages returned by performing a search of the plurality oflanding pages using relevant keywords using the campaign creation serversystem.

A further embodiment again also includes generating additional sourcedata by determining additional attributes associated with the landingpages using a semantic reasoner.

In another embodiment again, obtaining a list of relevant keywordscomprises generating a list of relevant keywords using at least onesource selected from the group consisting of search logs and a keywordtool using the campaign creation server system.

In a further additional embodiment, each category is identified using acategory name.

In another additional embodiment, the attributes are specified askey/value pairs.

In a still yet further embodiment, the taxonomy includes a set ofcategories and attribute values that describe products, which are thesubject of offers within the plurality of landing pages.

In still yet another embodiment, each product described within a landingpage is identified using a unique identifier that is derived from atleast one attribute that describes the product.

In as still further embodiment again, the taxonomy uniquely maps theplurality of landing pages to categories and attributes by mapping eachlanding page in the plurality of landing pages maps to a uniquecombination of category name and attribute values.

In still another embodiment again, the taxonomy maps each keywordcomponent to an individual category name or attribute value.

In a still further additional embodiment, mapping keyword components tocategories and attributes within the taxonomy comprises expanding thetaxonomy to include new category and attribute values to accommodate newkeyword components.

In still another additional embodiment, a grammar-less parser is used toparse the keywords into keyword components using the taxonomy.

In a yet further embodiment again, the categories are arranged as ahierarchy of categories with associated attributes.

In yet another embodiment again, the taxonomy supports recursivedefinition of a first category as an attribute of a second category.

In a yet further additional embodiment, building a taxonomy that mapsrelevant keywords to specific landing pages within the plurality oflanding pages includes identifying concepts representing uniquecombinations of categories and attribute values within the taxonomyusing the campaign creation server system, and predicting keywords thatare relevant to the identified concepts using the campaign creationserver system.

In yet another additional embodiment, a predetermined maximum number ofattributes is used when identifying concepts using the campaign creationserver system.

In a further additional embodiment again, identifying conceptsrepresenting unique combinations of categories and attribute valuesinclude identifying surface concepts using unique concepts of categoriesand attribute values, identifying deep concepts to which at least onesurface concept is subordinate, and predicting keywords that arerelevant to the identified concepts comprises predicting keywords thatare relevant to the identified deep concepts.

In another additional embodiment again, identifying deep conceptsfurther comprises inferring attributes of a deep concept based upon theattributes of a plurality of surface concepts.

In another further embodiment, the identified concepts comprise at leastone intent concept.

In still another further embodiment, predicting keywords that arerelevant to the identified concepts further comprises generating a setof keywords using keyword components within the taxonomy that areassociated with the category name and attribute values that form thebasis of the concept.

In yet another further embodiment, generating a set of keywords usingkeyword components within the taxonomy that are associated with thecategory name and attribute values that form the basis of the conceptfurther comprises generating keywords using the keyword components basedupon grammar patterns.

In another further embodiment again, the grammar patterns are associatedwith specific category names and attribute values within the taxonomy.

In another further additional embodiment, at least one of the grammarpatterns include a grammar component and a semantic component.

In still yet another further embodiment, at least one keyword isgenerated by combining the semantic components of a plurality of grammarpatterns in accordance with the grammar components of the grammarpatterns.

In still another further embodiment again, predicting keywords that arerelevant to the identified concepts further includes estimating keywordsearch volume using search volumes associated with the grammar patternsand keyword components used to generate the keyword, and filteringkeywords for relevancy to an identified concept based upon the estimatedkeyword search volume.

In still another further additional embodiment, identifying targetscomprises identifying at least one target for each relevant keywordbased on the relevant keyword and at least one other targeting criteriausing the campaign creation server system.

In yet another further embodiment again, the at least one othertargeting criteria is selected from the group consisting of keywordmatch type, language in a query indicative of intent, geographiclocation, profile information, language, and information derived from acookie contained within a browser application from which the searchquery originated.

Yet another further additional embodiment also includes identifying atleast one target for each identified concept within the taxonomy usingthe campaign creation server system.

In another further additional embodiment again, building a taxonomy thatmaps relevant keywords to specific landing pages within the plurality oflanding pages comprises mapping offer concepts to landing pages bymatching the attributes of the offer concepts to the attributes of thelanding pages using a campaign creation server system.

In a further embodiment, mapping offer concepts to landing pages furthercomprises mapping a landing page to an offer concept based upon theprecision and the recall of the landing page.

In another embodiment, the precision of a landing page is the number ofproducts on the landing page sharing the attributes of an offer conceptas a percentage of the number of products on the landing page.

In a still further embodiment, the recall of a landing page is thenumber of products on the landing page sharing the attributes of theoffer concept as a percentage of the total number of products listed onthe plurality of landing pages that share the attributes of the offerconcept.

In still another embodiment, building a taxonomy that maps relevantkeywords to offers further comprises testing a number of landing pagesto determine which is the most effective for a specific target and offercombination.

In another further embodiment, adgroups are generated includingcreatives that are automatically generated using the attributes of theoffer associated with the adgoup.

In still another further embodiment, the creatives are generated basedupon templates defined with respect to an adgroup.

In a yet further embodiment, automatically generating adgroups furtherincludes generating an adgroup for a target concept, where the adgroupincludes keywords relevant to the target concept using the campaigncreation server system.

In a further embodiment again, the concept to which the landing page ismapped is a target concept.

In another embodiment again, the concept to which the landing page ismapped is an offer concept.

In a further additional embodiment, the keywords included in the adgroupare the keywords predicted to be relevant to a target concept.

In another additional embodiment, a plurality of creatives is includedin the adgroup.

A still yet further embodiment also includes generating a plurality ofcreatives based upon a template and the attributes of an offer concepttargeted by a target concept using the campaign creation server system.

In still yet another embodiment, the targeting attributes associatedwith a list of keywords and creatives includes at least one attributeselected from the group consisting of the match type of the keyword, thegeographic location of the user, information derived from a cookielocated within the browser application used to submit the search query,and information derived from a user profile.

In a still further embodiment again, the offer concept is selected for aspecific target based upon the estimated performance of the offerconcept with respect to search queries targeted by the specific target.

In still another embodiment again, the performance of the plurality ofcreatives with respect to each target is tested and used to refine theselection of the specific creative to display with respect to a specifictarget.

In a still further additional embodiment, automatically generatingadgroups further includes associating a specific landing page with anoffer concept using the campaign creation server system, and associatingan offer concept with at least one creative.

Yet another additional embodiment also includes identifying new keywordstargeted by the adgroups and updating the taxonomy based upon the newkeywords using the campaign creation server system.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart illustrating a process for building a targetedsearch advertising campaign using a taxonomy in accordance with anembodiment of the invention.

FIG. 2 is a flow chart illustrating a process for mapping keywordcomponents to attribute values within a taxonomy in accordance with anembodiment of the invention.

FIG. 3 is a flow chart conceptually illustrating work flows associatedwith building and refining a taxonomy using a combination of automatedand human intelligence tasks in accordance with embodiments of theinvention.

FIG. 4 is a flow chart illustrating a process for mapping keywords toconcepts and identifying product and/or category landing pages toassociate with the concepts in accordance with embodiments of theinvention.

FIG. 5 illustrates a process for generating keyword, landing page,creative, and match type combinations for specific keywords within anadgroup in accordance with embodiments of the invention.

FIG. 6 is a conceptual illustration of a user interface illustratingcreatives templates that can be used to generate creatives for inclusionin an adgroup in accordance with an embodiment of the invention.

FIG. 7 is a flow diagram conceptually illustrating the creation of atargeted search campaign using adgroup templates and creative templatesin accordance with embodiments of the invention.

FIG. 8 is an architecture diagram of a system for building a targetedsearch advertising campaign using a taxonomy in accordance with anembodiment of the invention.

DETAILED DESCRIPTION

Turning now to the drawings, campaign creation systems and methods forbuilding taxonomy based targeted search advertising campaigns inaccordance with embodiments of the invention are illustrated. The termtaxonomy is used to describe a particular scheme for classifyingproducts and/or services. Collectively products and/or services (indeedany object, person, idea, or action) can be referred to as a conceptand, in many embodiments, concepts can be defined in terms of categoriesand attribute value pairs. In this way, a taxonomy used to buildtargeted search advertising campaigns can also include elements of anontology in the sense that the possible attributes of classifiedconcepts can also be specified as can the relationships between thoseattributes. In several embodiments, a taxonomy is constructed based uponkeywords actually used by people searching for the specific productsand/or services that are featured in an advertising campaign. In manyembodiments, the taxonomy is used to relate landing pages within awebsite or group of websites to categories and attributes. The taxonomycan also be used to identify relationships between keyword componentsand the categories and attributes within the taxonomy and theserelationships used to identify potentially relevant keywords for use intargeting a search advertising campaign with respect to specificconcepts defined by the categories and attributes within the taxonomy.Effective or optimal creatives can then be automatically generated basedupon the attributes of offers that are likely to perform well withrespect to specific queries targeted by the search advertising campaign.An offer can be considered to be the attributes of what is being offeredor presented to the user by the targeted search campaign. An offer caninclude product and/or service related attributes and can includenon-product/service related attributes, such as reviews. As is discussedfurther below, the set of attributes that define the offer can beutilized to identify landing pages relevant to a targeted search queryand to generate one or more creatives to display in response to thetargeted search query. In several embodiments, relevant landing pagesare determined using the offer attributes based upon their precision andrecall. In a number of embodiments, relevant queries can be targetedmore effectively by defining targets using keyword, keyword match typeand/or additional targeting criteria such as (but not limited to) thegeographic location from which the search query originated, informationcollected by cookies in a browser application, and/or information withina user profile. In this way, an entire targeted search advertisingcampaign can be built specifying which creative/landing page combinationto trigger in response to specific search queries.

In a number of embodiments, a set of products and/or services (i.e.concepts) advertised via one or more websites along with a list ofkeywords relevant to the products and/or services are processed togenerate a taxonomy. In a number of embodiments, the possible keywordsinclude known keywords and keywords generated by combining keywordcomponents from known keywords using known grammar patterns. The set ofpossible keywords is often referred to as the keyword space. In severalembodiments, the taxonomy maps the keywords in the keyword space todistinct concepts, and offer concepts can be identified within thetaxonomy that map to one or more creatives and one or more landingpages.

A taxonomy generated in accordance with embodiments of the invention canbe utilized to build a campaign. The campaign includes a set ofadgroups. The adgroups provide search engine providers with informationconcerning the creative and landing page combination to display when aspecific query occurs (i.e. in response to a specific target). Eachadgroup typically includes a landing page, at least one creative, and atleast one target. Although, any adgroup can be constructed appropriateto the requirements of a specific search engine provider in accordancewith embodiments of the invention. A creative is the specificadvertisement that is displayed as part of the search engine results anddifferent creatives can be displayed with respect to the same landingpage in response to different targeted search queries. Targets definethe search queries targeted by the adgroup and can be defined in termsof any attribute of a search query including but not limited to keywordswithin the search query, keyword match type, and additional informationthat can derived from the search query such as the IP address from whichthe search query originated, cookies in the browser application used toprovide the search query, and profile information associated with theuser that submitted the search query. Once a campaign is deployed, theselection of creative and landing pages that are displayed in responseto a keyword match can be refined to improve the performance of theoverall campaign. The generation of targeted search advertisingcampaigns using taxonomies in accordance with embodiments of theinvention is discussed further below.

Building Targeted Search Campaigns

A process for building a targeted search campaign using a taxonomy inaccordance with an embodiment of the invention is illustrated in FIG. 1.The process 10 can be performed by a campaign creation server system andincludes fetching (12) data related to landing pages included in thewebsite or websites that are being promoted by the campaign and datarelated to the manner in which people search for the products and/orservices that are featured by the website or websites. In severalembodiments, the landing pages can include category landing pages andspecific product landing pages. In many embodiments, the fetched dataincludes a list of keywords. As discussed below, the keywords can beused to define a keyword space based upon the list of keywords and a setof keywords generated using the keyword components of the keywords inthe fetched data and known grammar patterns. The fetched data is used tobuild (14) a taxonomy that maps landing pages to categories andattribute values. The taxonomy also maps keyword components to specificcategory and attribute values. Building a campaign (16) using thetaxonomy involves identifying concepts that can be targeted by thesearch advertising campaign based upon the categories and attributeswithin the taxonomy. The taxonomy can then be used to identify keywordsfrom within the keyword space that are potentially relevant to thetargeted concepts using grammar patterns and keyword components that aremapped to the category and attributes that form the basis of theconcept. These potentially relevant keywords can be used to definetargets for a search advertising campaign. The building of the campaignis completed by identifying the attributes of offers that are likely toperform well with respect to each of the targets (e.g. yield the highestclick throughs, conversion, and/or revenue). The attributes of theoffers can then be used to select landing pages and to generatecreatives. As is discussed further below, the process of building acampaign in accordance with many embodiments of the invention caninvolve the automatic generation of creative, landing page and targetcombinations. Once an offer is identified for each target and used togenerate a landing page, creative, and target combination, the campaigncan be deployed (18) to one or more search engine providers. In a numberof embodiments, the performance of the campaign is monitored and thechoice of landing page and creative for specific targets is modified inresponse to the performance of the campaign.

Although a specific process for building a paid search advertisingcampaign using a taxonomy is illustrated in FIG. 1, any of a variety ofprocesses can to build a taxonomy and to generate a paid searchadvertising campaign using the taxonomy can be utilized in accordancewith embodiments of the invention. Although much of the discussion thatfollows refers to products and categories of products, targetedadvertising campaigns in accordance with embodiments of the inventioncan be utilized to target advertising to concepts other than productsincluding (but not limited to) concepts related to services. Thecreation of a taxonomy, the use of the taxonomy to build a campaign, theautomatic generation of creatives, and the optimization of deployedcampaigns in accordance with embodiments of the invention are discussedfurther below.

Generating a Taxonomy

Construction of a targeted search advertising campaign for a number oflanding pages in accordance with an embodiment of the invention involvesbuilding a taxonomy that can be used to categorize all relevantkeywords. In several embodiments, the taxonomy is a lexicon orvocabulary of how people actually search for the specific products andservices that are being featured by the advertising campaign. Thetaxonomy maps keyword components to a specific category or attribute inthe taxonomy. In many embodiments, the taxonomy includes categoriesarranged hierarchically (e.g. Furniture/Chairs/Wood) and one or moreattributes defined using key/value pairs (e.g. Material: Birch). Whenthe taxonomy is organized as a hierarchy of categories with associatedattributes, the taxonomy can map keyword components to specific categorynames or attribute values. In other embodiments, a taxonomy can beorganized in any manner appropriate to the specific application. Forexample, the taxonomy can include recursion support and triples, and/ormore complex descriptive structures can be used in place of key/valuepairs to define attributes.

Obtaining Categories and Attributes from Source Data

The process of building a taxonomy in accordance with many embodimentsof the invention involves obtaining a set of source data describing theproducts and categories of products featured on the website or group ofwebsites that will be the subject of the campaign. In many embodiments,an initial crawl of the website or group of websites is performed.Typical crawls can include but are not limited to a product crawl, whichpulls key pieces of information about each product, and a categorycrawl, which determines the categorization structure of the website orgroup of websites. In a number of embodiments, a filter crawl is alsoperformed to obtain all relevant product filters on the site. In manyembodiments, search results pages returned by the site in response tosearch queries based upon keywords of interest are also crawled and thesearch results pages used as landing pages within a targeted searchcampaign. In certain embodiments, the site categorization structurebecomes the basis of category definitions within the taxonomy. Ineffect, the source data provides information concerning the attributesof the products and/or services that can be presented to a user by thelanding pages that are the subject of the advertising campaign. Inaddition to information about the products and/or services, the sourcedata can also include information concerning search queries such assearch logs, reports, keyword tools, and search query reports. In otherembodiments, any of a variety of sources of data can be used to obtaininformation related to the attributes of products and/or services thatwill be presented in the paid search campaign. For example, a semanticreasoner or inference engine can be utilized to determine additionalattributes associated with products and/or services. Accordingly, any ofa variety of sources of data concerning the attributes of productsand/or services can be utilized in the generation of a taxonomy asappropriate to the requirements of a specific application in accordancewith embodiments of the invention.

In many embodiments, the source data is processed to build ahierarchical taxonomy including a complete set of categories andattribute values that describe all of the products and/or servicesfeatured within the landing pages. In several embodiments, the taxonomyis constructed in such a way that each landing page maps to a uniquecombination of category and attributes. As noted above, the categoryhierarchy can be generated automatically utilizing the scrapedwebsite(s) categorization structure and/or manually. The attributevalues can also be added to the taxonomy automatically and/or by hand.In several embodiments, specific products within the source data are notallocated a unique source data ID. Instead, a key attribute orcombination of attributes or a hash of the key attribute or combinationof attributes is used as a unique identifier, which can be referred toas the source data ID, to identify each product. In many embodiments,the key attribute is one or a combination of (or a hash of one orcombination of) product ID, SKU, or URL. In the absence of suchattributes, the key attributes can be a top-level category name combinedwith a subcategory name, or city name combined with a state code. Inthis way, the campaign can change as the landing pages change.

Although much of the discussion that follows refers to taxonomiesconstructed using a combination of category names and attribute values,the category names can themselves be thought of as attribute values.Accordingly, the entire taxonomy at an abstract level can be consideredto use attribute values to categorize landing pages within a site orgroup of sites.

Mapping Keyword Components to Attributes and Taxonomy Generation

The process of building a taxonomy in accordance with embodiments of theinvention also involves parsing relevant search keywords into keywordcomponents that are the same as or are synonymous with category names orattribute values within the taxonomy. As noted above, the term keywordin the context of targeted search advertising can refer to a single wordor a combination of words. A keyword component refers to a single termwithin a keyword, such as “chair” or “bar stool”, that can be mapped toa single category name or attribute value within the taxonomy. In manyinstances, multiple synonymous keyword components are mapped to the samecategory name or attribute value within the taxonomy. Where the keywordcomponent is neither the same nor a synonym of a category name orattribute value within the taxonomy, a decision can be made concerningwhether to discard the keyword component or to expand the taxonomy toinclude the keyword component. In several embodiments, sources ofkeywords can include manual collection of keywords and/or the automatedcollection of keywords.

A process for mapping keyword components from relevant keywords toattribute values within a taxonomy in accordance with an embodiment ofthe invention is illustrated in FIG. 2. The process 30 involvescollecting (32) possibly relevant keywords and then classifying (34) thekeywords according to their relevance. Relevant keywords can be obtainedfrom a variety of sources including but not limited to search logs andkeyword tools. In many embodiments, determining the relevance ofkeywords is a human intelligence task (i.e. a task performed by a humanoperator). Relevant keywords are parsed (36) to break them intocomponents that match categories and attribute values within thetaxonomy. Parsing of keywords into keyword components is describedbelow. In many embodiments, when the keyword component matches multiplecategories or multiple attribute values within the taxonomy, the keywordcomponent can be mapped to each In other embodiments, this ambiguity maybe resolved with a human intelligence task (i.e. a task requiring theinvolvement of a human), or by using any number of algorithms that canresolve ambiguity. If a keyword component doesn't match the taxonomy,then a determination is made concerning whether the keyword component issynonymous with an existing category name or attribute value, or whetherthe keyword component represents an entirely new attribute value. Thereare a number of algorithms that can guess what category or attributevalue a keyword component may be synonymous with, or that can resolveambiguity. For example, a thesaurus, or the similarity of the letters inthe word to the letters in the category/attribute value or its synonyms.To resolve ambiguity, a data source such as Wikipedia disambiguationpages or using machine learning techniques using examples of othercorrectly-disambiguated keywords may be used. This determination can beautomated or automated in combination with validation as a humanintelligence task. When the keyword component represents a new attributevalue, the taxonomy is expanded (38) to accommodate the new keywordcomponent. As discussed above, many taxonomies utilize a hierarchy ofcategories and a number of different attributes can be used to describea specific category. Therefore, expanding the taxonomy can involveadding a new category, attribute and/or attribute value to the taxonomy.In this way, the taxonomy can continue to expand with the goal ofmapping all relevant keyword components to a category name or attributevalue within the taxonomy.

Although a specific process for mapping relevant keyword components tocategory names or attribute values is illustrated in FIG. 2, any of avariety of processes appropriate to specific applications can also beutilized in accordance with embodiments of the invention includingprocesses that can be performed automatically and/or manually.

Parsing Keywords into Keyword Components

In many embodiments, parsing of keywords involves the use of agrammar-less parser, which utilizes the taxonomy to parse the keywordsinto keyword components instead of a grammar. Components of the keywordsmap to category names and attribute values within the taxonomy.Therefore, the taxonomy can be used to identify grammatical rules. As isdiscussed further below, grammatical rules that are learned during theparsing of keywords can be used to predict “new” or previously unseenkeywords in accordance with embodiments of the invention. Thesepredicted keywords can be incorporated into a targeted search campaignso that the campaign is not limited to only those keywords included inthe source data.

Although the use of grammar-less parsing is discussed above, a parserthat utilizes a grammar and/or any of a variety of other parsers can beutilized in accordance with embodiments of the invention to parsekeywords into keyword components.

Taxonomy Creation Using a Combination of Automation and HumanIntelligence Tasks

The process of building a taxonomy from source data, obtaining sourcesof relevant keywords and then mapping each keyword or keyword componentto a category name or attribute value within the taxonomy can becomplex. In many embodiments the process is automated. In severalembodiments, a hybrid approach to building a taxonomy is utilizedcombining both automation and human intelligence tasks. A workflow forbuilding a taxonomy with the assistance of human decision makers isillustrated in FIG. 3. The workflow process 40 shown in FIG. 3 utilizesthe Mechanical Turk marketplace provided by Amazon.com, Inc. of Seattle,Wash. for the performance of human intelligence tasks associated withthe creation of the taxonomy. The workflow process 40 includes threebasic workflows. A keyword processing workflow 41, a human intelligencetask workflow 42 and a taxonomy improvement workflow 43.

The keyword processing workflow 41 is similar to the process illustratedin FIG. 2 (described above) and basically involves parsing (43) keywordcomponents from keywords obtained from keyword sources (44) andidentifying keyword components that are ambiguous or not already mappedto an attribute value within the taxonomy. The handling of ambiguous or“new” keyword components can involve automated processes and/or humanintelligence tasks.

In the illustrated embodiment, the human intelligence task workflow 42involves providing the “turks” (i.e. the people performing the humanintelligence tasks (45)) with templates in which the turks can enterattribute data for each product and/or service landing page, and foreach category landing page. In addition to providing templates,instructions are typically also provided concerning the meaning of eachattribute in the taxonomy to increase the likelihood that attributeinformation is entered consistently. The turks then process (46) all ofthe source data concerning the products and/or services, which involvesentering the different attribute values for the products and/or servicesfeatured in the landing pages into the templates. In many embodiments,the human intelligence tasks are performed by the turks on a smallsample (47) and the taxonomy, templates, and/or instructions are refineduntil a decision (48) to proceed is made the full dataset can beprocessed (46).

In the illustrated embodiment, both the keyword processing (41) andhuman intelligence task (42) workflows place attribute values or keywordcomponents into a taxonomy improvement queue. The determination ofwhether the keyword component or attribute value is relevant, a synonymof an existing category name or attribute value, a new value for anexisting category or attribute, or an entirely new attribute or categorycan be a human intelligence task performed by an editor or by turksand/or involve an automated process. In several embodiments, newcategories, attributes and keyword components are placed in a taxonomyimprovement queue (49) and automated processes and/or human intelligencetasks are performed to determine whether an addition should be made tothe taxonomy. The workflows illustrated in FIG. 3 continue until all ofthe source data is processed and all of the keyword components andattribute values are cleared from the taxonomy improvement queue oruntil a sufficiently high keyword space coverage is achieved. Keywordspace coverage can be measured as the percentage of input keywords, inmany embodiments weighted by search volume, that can be mapped to aconcept defined by the taxonomy. Once a predetermined target keywordspace coverage is achieved, the taxonomy can be utilized to build atargeted search campaign in accordance with embodiments of theinvention.

Although a specific process is illustrated in FIG. 3, any of a varietyof fully automated, manual, or hybrid processes that map data from a setof source data to attribute values within a taxonomy can be utilizedinto the building of a taxonomy in accordance with embodiments of theinvention.

Building a Targeted Search Campaign Using a Taxonomy

The process of building a targeted search campaign in accordance withembodiments of the invention involves predicting relevant keywords andthen using relevant keywords to identify targets for the advertisingcampaign using the taxonomy. The identified targets are mapped to one ormore relevant landing pages and creatives. Processes for identifyingtargets, generating creatives, and building targeted search campaignsusing taxonomies in accordance with embodiments of the invention arediscussed further below.

Associating Keywords with Targets

A keyword space is a theoretical space containing all potentiallyrelevant keywords identified in terms of a set of category and/orattribute values. Campaigns in accordance with many embodiments of theinvention identify targets for the targeted search campaign based uponkeywords in a keyword space. In many embodiments, identifying targetsinvolves selecting a subset of keywords in the keyword space that arebelieved to be useful, relevant keywords. The process of selectingkeywords can involve mapping known “good” keywords to concepts and/orgenerating/predicting relevant keywords using known keyword componentsand grammar patterns. In several embodiments, the process of identifyingtargets involves defining concepts representing unique combinations ofcategories and attribute values within the taxonomy, then using thecombination of categories and attribute values to predict keywordsrelevant to the concept. In this way, relevant keywords can be predictedusing the taxonomy and targets identified by combining these relevantkeywords with other relevant targeting criteria.

A process for identifying targets in accordance with an embodiment ofthe invention is illustrated in FIG. 4. When the landing pages have beentaxonomized (see discussion above), the process 50 builds (54)“concepts” for unique combinations of category names and attributevalues from the taxonomy. Concepts can generally be considered toreflect the meaning behind a specific search query. Once a set ofconcepts is defined using the taxonomy, a set of keywords that maps toeach of the concepts is determined (56). In many embodiments, attributesassociated with the concept can be determined by targeting criteria orattributes other than the search query, such as the user's geographiclocation, age, or gender. For example, the concept [Men's Shoes],represented by the attributes CATEGORY=Shoes, GENDER=Male, may be linkedto the keyword “shoes” combined with the Male targeting criteria. Oncethe concepts and associated relevant keywords are determined, one ormore targets can be generated for each concept using the keywords incombination with other relevant targeting criteria. The process 50 canalso involves generating (58) one or more targets for each concept.

A target is a specific search query or set of search queries that are tobe targeted by a specific advertisement in the targeted search campaignand can include any of a variety of attributes derived from the queryitself, IP addresses, browser cookies, and/or a user profile associatedwith the user that provided the query. The extent to which a searchquery can be targeted is not limited to the keywords utilized in thesearch query. Additional information can also be derived from a searchquery. For example, information concerning where a user is in thepurchase funnel can be derived from language in the query indicative ofintent (e.g. “customer reviews for KEYWORD”, or “price of KEYWORD”).Accordingly, the same keyword can be targeted in different waysdepending upon intent. In many instances, the campaign may choose toavoid targeting a keyword where it is unlikely to lead to the acceptanceof an offer (e.g. “KEYWORD repair” is not related to the sale of theKEYWORD). Additional information can also be indirectly derived from aquery such as geographic location. The IP address of the device fromwhich a search query originates can be used to identify the geographiclocation of the IP address. Accordingly, keywords can be targeted indifferent ways in different geographic locations (e.g. “KEYWORD” in IPAddress maps to Santa Monica, Calif. vs. “KEYWORD” in IP Address maps toGlendale, Calif.). Accordingly, targets in accordance with embodimentsof the invention can include any of a variety of attributes that can bederived from a search query, including but not limited to attributesderived from the query itself, IP addresses, browser cookies, and/or auser profile associated with the user that provided the query. Themapping of keywords to concepts, and the generation of targets using theconcepts are discussed further below.

Generating Concepts Using the Taxonomy

Concepts can be generated using unique combinations of category namesand attribute values from a taxonomy. For example, the category “BarStools” and the attribute key value “Material: Wood” form the concept“Wood Bar Stools”. A concept can be generated for every possiblecombination of category name and attribute value. In many embodiments,however, concepts are limited to a specified number of attributes. Inseveral embodiments, intent can also be incorporated into the identifiedconcepts. Such concepts can be referred to as intent concepts. Theintent captured in the intent concept reflects the intention of userssearching for the concept. As is discussed further below, intentconcepts can be utilized to better target presentation of offers tousers.

Many different concepts generated using category names and attributevalues can have the same meaning. In a number of embodiments, conceptsobtained by building unique combinations of category name and attributevalues from the taxonomy are regarded as “surface concepts”. Oftensurface concepts, although unique, refer to the same thing or aresynonymous. For example, the surface concepts “ps 2” and “SonyPlaystation 2 Video Game Console” actually refer to the same video gameconsole. One surface concept includes MODEL and VERSION, whereas theother has BRAND, MODEL, VERSION, AND CATEGORY. The video game console towhich both surface concepts refer can be considered a “deep concept”. Adeep concept is a single canonical concept and typically includes all ofthe category name and attribute values that can be used to describe theconcept. Stated another way, the deep concept holds all of theattributes of the concept and so describes the concept at its mostgranular.

When the distinction is made between surface and deep concepts, keywordsare mapped to deep concepts as opposed to surface concepts. Use of deepconcepts can simplify the construction of a campaign by combining allkeywords with the same meaning into a single adgroup. In addition, deepconcepts hold all of the information related to a concept and enablemore complete mappings of surface concepts to offers. In manyembodiments, the process of mapping deep concepts to surface concepts isrelatively straightforward. The deep concept includes more attributesthan the surface concept and the attributes of the surface concept mapto a single deep concept. Where the attributes map to multiple deepconcepts, then a disambiguation process can be used that can be a humanintelligence task and/or include an automated process. Disambiguationprocesses that can be utilized to disambiguate the mapping of phrases toconcepts in accordance with embodiments of the invention are discussedfurther below. The attributes of the deep concept can be useful inunderstanding how a surface concept relates to other concepts. Forexample, systems in accordance with embodiments of the invention caneasily calculate that [CITY=Los Angeles, STATE=California] issubordinate to [STATE=California]. A concept is subordinate to anotherconcept when the subordinate concept is included within or is a subsetof the other concept. However, the system may not know that the surfaceconcept [CITY=Los Angeles] is subordinate to [STATE=California].However, if [CITY=Los Angeles] is first mapped to deep concept [CITY=LosAngeles, STATE=California], then the system can establish therelationship between the two surface concepts. Where attribute valuesare stored in a hierarchy, systems in accordance with embodiments of theinvention can use the hierarchy to map surface concepts to deepconcepts. A thesaurus can also be used to map surface concepts to a deepconcept as can transformation rules that specify how a concept followinga specific pattern (including but not limited to a grammar pattern)should map to a deep concept.

An inference engine may also be used to infer additional attributes fromthe attributes of the surface concept. In many embodiments, the set ofall attributes inferred from the surface concept can be added to theattributes of the surface concept to obtain the deep concept. In thisway, the deep concept can hold all of the attributes related to aconcept. Once a set of deep concepts have been established, then themanner in which potentially relevant keywords map to the deep conceptscan be determined in accordance with embodiments of the invention.

Mapping Keywords to Concepts

In many embodiments, the set of keywords that map to a concept isgenerated using all possible combinations of the synonymous keywordcomponents within the taxonomy that are associated with the categorynames and attribute values that form the basis of the concept. Forexample, if the concept is “Wood Bar Stools” and the taxonomy associatesthe synonymous keyword concepts “bar stools”, “barstool”, “barstools”,and “bar stool” with the “Bar Stools” category and the synonymouskeyword concepts “wood”, and “wooden” for the “Wood” attribute then thekeywords that map to the concept are “wood bar stools”, “wooden barstools”, “wood barstool”, “wooden barstool”, “wood barstools”, “woodenbarstools”, “wood bar stool”, and “wooden bar stool”.

The combination of keyword concepts used in the above example can alsoyield the keywords “bar stools wood” etc., which are unlikely to be usedin search queries. In many embodiments, therefore, the generation of aset of keywords that map to a concept also considers grammar patterns.Grammar patterns can be defined that specify the order in which categorynames and attribute values are combined. In many embodiments, thegrammar patters include both a grammar component and a semanticcomponent. Accordingly, the grammar patterns specify how specifickeyword components can be used in specific contexts. In this way,combinations of grammar patterns are utilized to generate keywords. Thecombination of grammar patterns involves combining the semanticcomponents of the grammar patterns in accordance with the associatedkeyword components. For example, in a video game taxonomy grammarpatterns can be derived such as ‘VIDEOGAME_RELEASE game’, ‘modernwarfare CATEGORY’ and ‘video game’. These grammar patterns identify thatthe keyword component ‘game’ can be modified by VIDEOGAME_RELEASE, butcan't necessarily stand alone as a keyword. The ‘video game’ keywordcomponent, however, can stand alone as a keyword. And the keywordcomponent ‘modern warfare’ can modify CATEGORY. These grammar patternscan be utilized to produce keywords as follows: ‘VIDEOGAME_RELEASEgame’+‘modern warfare CATEGORY’ produces ‘modern warfare game’, where‘modern warfare’ is a value of the VIDEOGAME_RELEASE attribute and‘game’ is a CATEGORY name. Keyword produced in this way are associatedwith their semantic components (VIDEOGAME_RELEASE and CATEGORY in theabove example), and can thereby be distinguished from keywords that areidentical but have different semantic components (ambiguous keywords orhomographs). This allows ambiguous keywords or to be treated asdifferent keywords.

In other embodiments, simpler grammar patterns that do not incorporatesemantic content can be utilized that specify the ordering of roots andmodifiers, and any static text that should go before, after, or inbetween attributes. A root is a keyword concept synonymous with acategory name. A modifier is a keyword concept synonymous with anattribute value. In the example of the “Wood Bar Stools” conceptdescribed above, the specified grammar pattern is MATERIAL CATEGORY. Inmany embodiments, specific grammar patterns are associated with specificcategories (e.g. MATERIAL CATEGORY: “Bar Stool”).

As is discussed below, the incorporation of semantic components intogrammar patterns can significantly decrease the need to disambiguatekeywords generated using the grammar patterns. The same keywordcomponents and grammar patterns used to generate keywords associatedwith a particular concept can also be used to generate dynamic displaynames for use in creatives (see discussion below).

In many embodiments, the search volume and frequency of a grammarpattern and the keyword components combined using the grammar patterncan be used to help predict the likely search volume or frequency ofkeywords generated using the grammar pattern. The most frequent keywordscan be those chosen for use in the targeted search campaign. Whengenerating keywords, predicting search frequency based upon the searchfrequency of the keyword components and grammar patterns can be used tofilter the generated keywords to predict the generated keywords that aremost likely relevant to the targeted search campaign.

When mapping keywords to concepts, ambiguity can exist due to a singlekeyword mapping to multiple concepts. Incorporation of semanticcomponents into grammar patterns can significantly reduce the need toperform disambiguation. In addition, use of machine learning to identifyadditional grammar patterns (including negative or incorrect grammarpatterns) can further reduce the need for performing separatedisambiguation processes. When ambiguity is present, a disambiguationprocess can be utilized to select the concept with which to associate akeyword. In many instances, probabilities can be assigned to eachpossible interpretation. In a number of embodiments, each possibleinterpretation is weighted based upon its estimated search volume. Inmany embodiments, the search volume estimate is based on the knownsearch volume of the grammar patterns that make up the keyword,including the semantic components of those grammar patterns (such thattwo keywords with the same spelling but different semanticcomponents—homographs—may have different scores). In severalembodiments, disambiguation is a human intelligence task and/or involvesautomated processes. In many embodiments, blacklists are maintained toprevent specific ambiguous keywords from being associated with specificconcepts.

Although specific processes for generating keywords using keywordcomponents synonymous with the category name and attribute valuesincluded in a concept are discussed above, any manual or automaticprocess for mapping keywords to concepts that is appropriate to aspecific application can be utilized in accordance with embodiments ofthe invention.

Deriving Grammar Patterns

Grammar patterns can be derived from unparsed keywords with volume/valuedata that have unambiguous parsings. In addition, a machine learningsystem can derive additional grammar patterns and negative grammarpatterns from a training data set including ambiguous parsings and thecorrect mappings for those keyword components and incorrect mappings forthose keyword components. As the database of grammar patterns grows, thesystem is able to correctly parse more keywords from the known list ofkeywords. The system can then extract additional grammar patterns fromthese correct parsings, allowing it to correctly parse even morekeywords. In this way, the ability of the system to correctly parsekeywords and to generate relevant keywords using keyword components andgrammar patterns associated with specific concepts can improve overtime.

Generating Targets

Target concepts are typically defined in terms of the commercial intentof a search query or an intent concept. A target may include a keywordin addition to any targeting criteria associated with a search query. Asimple target can be a keyword that has been generated for a targetconcept or more specifically a deep concept. More complex targets caninclude search query attributes indicative of a consumer's geographiclocation, age, gender, or other characteristics that may be of interestto a marketer and/or connected to the target concept or consumer intent.In many embodiments, additional attributes of a search query can betargeted including but not limited to information derived from a user'sIP address, a cookie within the browser application used to submit aquery, and/or a user profile. In many embodiments, these additionalattributes may be selected using an inference engine that selects thoseattributes of a search query that are likely to be present for consumerswith intent indicated in the given intent concept. For example, a targetconcept may include attributes CATEGORY=Shoes and GENDER=Male. A targetmay be generated for this target concept that includes all searchqueries for the keyword “shoes” made by men. Or, it may include allsearch queries for the keyword “men's shoes”. The extent to whichadditional attributes are included in the selection of targets is oftendetermined based upon the requirements of a specific application.

Targeting Offer Concepts

An offer concept defines the attributes of an offer that is presented toa user in response to a targeted search query. As is discussed furtherbelow, the attributes of an offer concept are not necessarily the sameas the attributes of the concept used to generate a target (i.e. thetarget concept). Whereas a target concept describes what a user issearching for, an offer concept describes what the website(s) that arethe focus of the paid search advertising campaign can offer the user. Inmany embodiments, a campaign creation system attempts to match theoffers (i.e. the offer concepts) that are likely to yield the bestoutcome (e.g. click throughs, conversions, revenue) with respect to aspecific target. Once an offer concept is matched to a target, theattributes of the targeted offer concept can be utilized to select alanding page and to generate creatives that can be used to present theoffer to a user in response to a targeted search query.

A target concept does not necessarily correspond to an offer concept(i.e. a set of attributes presented in an advertisement). The offerconcept is expressed as a combination of actual offer attributes (e.g.“LCD TVs”). Although the target concept is often also just a combinationof attributes, it might contain attributes that aren't an actual part ofthe offer. For example, (e.g. “Comparison LCD and Plasma TVs”). In manyinstances, the advertiser's landing pages may not include a“comparison”. The advertiser offers TVs, or LCD TVs, or Plasma TVs. Sothe target concept is mapped to one of these offer concepts. In general,once a target concept has been unambiguously mapped to a deep concept,it is mapped to an offer concept as part of the process of determiningrelevant ads and landing pages. There could be various rules for makingthis mapping, such as choosing one of the 2 concepts being compared asthe offer concept, or choosing the most common ancestor (e.g.televisions).

Another example of target to offer concept mapping is mapping theconcept:

-   -   “Dentists Near Santa Monica”, which is defined using the        attributes OFFER TYPE=Small Business/Dentists and NEAR        LOCATION=California/Los Angeles/Santa Monica

to the offer concept

-   -   “Dentists in Venice Beach”, which is defined using the        attributes OFFER TYPE=Small Business/Dentists NEAR        LOCATION=California/Los Angeles/Venice Beach)

Such a mapping can make use of a “fact base” containing information suchas the fact that Venice Beach is near Santa Monica. Although specificprocesses are outlined above for determining relevant landing pages forspecific targets including processes that involve mapping targetconcepts to offer concepts, any of a variety of processes can beutilized to identify one or more relevant landing pages for a specifictarget in accordance with embodiments of the invention.

In many embodiments, one or more offer concepts are mapped to a targetconcept based upon the target concept and offer concept combination thatis likely to maximize or improve a predetermined metric such as (but notlimited to) click throughs, conversions, and/or revenue. In a number ofembodiments, a machine learning process is used to map offer concepts tothe intent concepts underlying a specific target. Appropriate machinelearning processes include (but are not limited to) a process based upona Gaussian Art Map, Group Lasso, decision trees or any other appropriatemachine learning technique that can utilize semantic metadata orfeatures (i.e. semantic information describing a concept within thetaxonomy) to estimate performance of mapping a specific target to aspecific offer concept. In several embodiments, a campaign creationserver system used to build and deploy a campaign tracks metricsconcerning the performance of the campaign including (but limited to)total impressions, click throughs, conversion and revenue for eachintent concept and offer concept combination. This information can beutilized to generate reports and analytics concerning that attributesthat have the most influence on the performance of different intentattributes. The aggregated performance data is also used in the mannerdescribed above to select specific intent concept and offer conceptcombinations that will improve the performance of the overall campaign.

In several embodiments, offer concepts that are matched to a target areconstrained to offer concepts corresponding to products that are part ofthe advertiser's inventory of products and services. In manyembodiments, this inventory is the set of products and/or servicesrepresented on the landing pages in the advertisers web site. In manyembodiments, this inventory is determined from additional source dataobtained about the advertiser's products and services, for example fromthe advertiser's product catalog. In several embodiments, this inventorymay additionally be determined from the content of the creative andkeywords in the advertisers existing search advertising campaign. Inmany embodiments, offer concepts are selected based upon custom rulesselected by the marketer conducting the targeted search advertisingcampaign. For example, a marketer might direct a campaign creationserver system to offer the top selling product when the customer hasbrand intent, which translates to selecting the best intentconcept/offer concept combination where the intent concept includes theBRAND attribute and the offer concept includes the PRODUCT attribute.

Mapping Targeted Offer Concepts to Landing Pages

The creation of a targeted search campaign involves specifying targets,identifying one or more offers that are relevant to the targets (i.e.the targeted offer concepts) and generating creatives and selectinglanding pages based upon the attributes of the offers. For each offer,there may be multiple relevant landing pages and a mapping betweentargets and landing pages can be made using the attributes of the offerconcept targeted by a specific target. In a number of embodiments, thelanding pages that are mapped to a specific target are determined basedupon precision and recall with respect to the targeted offer concept.The precision of a landing page can be determined based upon the numberof products represented on the landing page that are subordinate to thetargeted offer concept as a percentage of the total number of productsrepresented on the landing page. The recall of a landing page withrespect to a targeted offer concept is the number of productsrepresented on the landing page that are subordinate to the targetedoffer concept as a percentage of the total number of products in theadvertiser's inventory (i.e. products that appear on any landing page)that are subordinate to the offer. Based upon the precision and recallof specific landing pages to a targeted offer concept, a match can bedetermined. In many embodiments, the landing page matched to a specifictarget can be determined based upon a target concept (i.e. the conceptindicating the intent of the search query). In several embodiments, afeatured offer page can also be selected where present. In manyembodiments, page scoring can be performed based upon customer ratingand/or price with the product landing page with the highest customerrating selected and the highest price offer acting as a the breakerbetween product landing pages featuring similar customer ratings. As isdiscussed further below, a number of landing pages can be utilized whenbuilding creatives based on a targeted offer concept and the system cantest each of the landing pages and determine which is the most effectivefor specific targets. If there is no closely or matching landing page,then the concept can be ignored or placed in a low-quality group.

Generation of Dynamic Display Names

Dynamic display names can be generated for each targeted offer conceptand the dynamic display names can be utilized in the generation ofcreatives that are displayed in response to a query matching therelevant target. In a number of embodiments, the generation of dynamicdisplay names is similar to the process described above for generating aset of keywords for a specific concept. The difference, however, is thatinstead of generating a set of keywords the process is used to generatea single phrase. In a number of embodiments, the phrase is selected tobe the shortest or most common form of the phrase. In severalembodiments, the grammar pattern, and keyword components that haveeither the highest search volume or the most impressions, or that haveappeared in creatives that received the most clicks or conversions, areused to generate the dynamic display name for an offer concept. In otherembodiments, any of a variety of techniques appropriate to a specificapplication can be utilized to generate a dynamic display name inaccordance with embodiments of the invention.

Campaign Building

When keywords have been mapped to concepts, concepts have been used togenerate targets, one or more relevant offer concepts have beenidentified for each target concept, and one or more relevant landingpages have been identified for each offer concept, using a processsimilar to the processes outlined above, a targeted search advertisingcampaign in accordance with embodiments of the invention can be built.The process of building the campaign involves creating adgroups, whichare lists of keywords and creatives with associated settings, such asbut not limited to the match type, IP target, or other targetingattributes of the ad group. These adgroups then form the basis of atargeted search campaign that can be uploaded to a search engineprovider. The search engine provider uses the adgroups to define thecreatives to display when a search query is received that matches one ofthe keywords within the adgroup in accordance with the match typespecified in the adgroup. Processes for building adgroups in accordancewith embodiments of the invention are discussed further below.

Building Adgroups

In many embodiments, an adgroup is generated for each target concept. Asnoted above, an offer concept is typically matched to each targetconcept to translate the intent of the query into an offer that can bepresented by the website(s) that is the focus of the targeted searchadvertising campaign. Creatives can be generated using the attributes ofthe targeted offer concept and one or more relevant landing pages havingdifferent degrees of specificity can be identified using the targetedoffer concept. In many embodiments, a separate adgroup is generated foreach landing page that is associated with each target concept (one adgroup per target concept/landing page combination). The targets includedin each adgroup are derived from the keywords associated with the targetconcept that is the basis of the adgroup. For each ad group, at leastone creative and landing page combination is defined along with thematch type of the keyword. As is discussed further below, more than onecreative and landing page combination may be defined for each keyword toaccommodate targeting of the advertising based upon such characteristicsas geographic location. In a number of embodiments, a set of creativesis created using the attributes of the targeted offer concept and theperformance of the different creatives with respect to each target istested and used to refine the selection of the specific creative todisplay with respect to a specific target. In many embodiments, however,the traffic for a number of the keywords is too low to extractmeaningful data concerning the performance of different creatives.Therefore, the creatives that are displayed in relation to specifickeywords is defined during the building of the campaign and modifiedbased upon analysis of the performance of the campaign. When adgroupshave been generated with respect to all of the target concepts withinthe taxonomy, the adgroups can be converted into bulk sheets that can beuploaded to one or more search engine providers to commence the targetedsearch campaign.

Building Adgroups Using Templates

Adgroups and creatives can be constructed using templates. A process forgenerating an adgroup using adgroup and creative templates in accordancewith an embodiment of the invention is illustrated in FIG. 5. Theprocess 70 commences by selecting (72) an adgroup template for theadgroup. In several embodiments, the adgroup template is determinedbased upon the type of landing page. For example, a different adgrouptemplate can be used depending upon whether the adgroup links to aproduct landing page or a category landing page. The difference in theadgroup templates may be as minor as specifying different URLs todisplay. In many embodiments, the adgroup template is selected based onthe offer concept and the attributes present in the offer concept.However, different adgroup templates typically include differentcreative templates. Once an adgroup template has been selected, creativetemplates are selected (74) and used to generate creatives based uponthe attributes of a targeted offer concept. A set of creative templatesis defined for each ad group template. In several embodiments, the firstcreative template that fits (i.e. satisfies formatting requirements forthe creative imposed by search engine providers) is selected and used togenerate (76) a creative using the attributes of a targeted offerconcept.

A user interface used to edit creative templates in accordance withembodiments of the invention is illustrated in FIG. 6. In theillustrated embodiment, the user interface 80 indicates that a singleadgroup template exists (i.e. irrespective of the inputs in the adgrouptemplate, the same creative templates are utilized to generatecreatives) and a list of creative templates are provided based upon theattribute values present in the offer concept. If the “Book Name”attribute value is present in the offer concept, then the first creativetemplate that fits the formatting requirements of the search engineprovider is selected. Otherwise, creative templates defined for otherattribute values are tested. In the illustrated embodiment, the creativeis generated by inserting the relevant attribute values associated withthe offer concept that forms the basis of the adgroup into the template.The first creative template 82 includes the static text “at Target” inthe title of the creative. If the number of characters of the title istoo long after substituting the PRODUCT_TITLE attribute of the offerconcept into the creative, then the second creative template 84 does notinclude the “at Target” static text to reduce the length of the title.In many embodiments, the creative template may include a placeholder forthe display form of the offer concept described above. In manyembodiments, the creative template may be generated automatically by thesystem from examples of human-written creatives associated with offers,by replacing attributes of the offer found in the creative with aplaceholder for the attribute name. For example, if a creativeassociated with an offer containing attributes CATEGORY=Televisions andSCREEN_TYPE=LCD contains the text “Deals on LCD TVs”, then this text canbe converted to the template “Deals on {SCREEN_TYPE} {CATEGORY}”. Usingtemplates in the manner outlined above, a creative can be generated foreach adgroup. A process for generating adgroups and uploading a bulksheet to the Google search engine provided by Google, Inc. isillustrated in FIG. 7. Although the use of adgroup templates andcreative templates is described above with respect to the generation ofcreatives for a specific adgroup, in many embodiments other techniquesappropriate to specific applications can be used to generate creativesfor inclusion in adgroups in accordance with embodiments of theinvention. In other embodiments, any of a variety of techniques can beutilized to provide the details of a targeted search advertisingcampaign to a search engine provider in accordance with embodiments ofthe invention.

Generation of Creatives Using Advertising Strategies

Another approach for generating creatives for inclusion in adgroups inaccordance with embodiments of the invention is to define “strategies”for selecting creative templates. A strategy associates a target with aspecific landing page and a specific set of creative templates. Forexample, a strategy could associate any targets within a geographic areasearching for reviews of televisions with a featured offer landing pagefor the highest rated television, and a creative theme that shows arating. Due to the fact that the strategy associates both a landing pageand a creative with a specific target, both the landing page and thecreative can be optimized over time to improve the performance of thestrategy.

Selection of Adgroup Match Type

The selection of a broad match type for an ad group can result in thekeywords submitted to the search engine operator, sometimes referred toas the bidded keywords, matching many different actual user queries. Asa result, specific search queries may “match” different bidded keywords,in which case the creative and landing page combination that isdisplayed within the search results is left to the search engineprovider to decide.

When the set of user queries matched by two different targets overlap,then it can be said that one target is “stealing” traffic from another.In conventional search advertising campaigns, targets can steal asignificant volume of traffic from each other unless very restrictivematch types are used. The segment of traffic that is captured by aspecific target can be defined as all user queries that match the targetless the user queries that are stolen by overlapping targets. Whenviewed in this way, the segment of traffic matched by a target isdetermined not only by the target itself, but other targets that arecurrently active in the ad campaign.

Understanding the segment of traffic captured by a target is furthercomplicated by the fact that bids and quality scores for differentcreative/landing page combinations change over time. Therefore, whenoptimizing an advertising campaign (see discussion below) smallerchanges are typically performed so that the changes do not significantlyalter the traffic segments for the targets. In many embodiments, thegoal of the optimization is to filter targets that are not drivingtraffic to the website or group of websites targeted by the campaign orthat are driving traffic that is unlikely to accept the presented offerand to modify the selection of creative and landing pages to improve theperformance of the campaign (i.e. acceptance of offers) with respect tothe targets that are effective in driving traffic to landing pages. Theoptimization process can be complicated by the statistically smallnumber of samples that may exist with respect to specific targets.Therefore, the optimization processes can be improved by groupingsamples for targets based upon common attributes of targets, such as(but not limited to) targets that include the same brand, product type,IP location, or any combination of these attributes keywords that map tothe same concept. Multivariate testing can then be applied to help makedecisions concerning targets to try as the campaign progresses. Inaddition, the most effective targets, and the most effectiveadvertisement/landing page combination for each target are likely tochange over time. Therefore, continued modification of the campaign canbe important to sustaining the performance of a campaign.

Keyword Discovery Using Controlled Expansion of Match Types

When a match type for a particular keyword is broadened, the keywordsthat are determined to match a target within a paid search campaign caninclude previously unknown keywords. In a number of embodiments,keywords that are “discovered” in this way can be used to improve thetaxonomy and the performance of the overall campaign. In manyembodiments, the match type of a specific keyword is broadened and anumber of new keywords are discovered. These keywords can be used torefine the taxonomy. New creative and landing page combinations can bedefined for targets that include the newly discovered keywords andnarrower match types can be utilized for the original target and the newtargets to further refine the performance of the campaign.

Campaign Optimization

In a number of embodiments, a campaign built using processes similar tothose described above is deployed in combination with an existingcampaign. In many instances, a marketer with domain expertise will havehighly optimized certain keyword, creative, and landing pagecombinations. By deploying a hybrid campaign, these highly optimizedkeywords, creatives, and landing page assignments can be incorporatedinto a targeted search advertising campaign generated using a taxonomy.Over time, the performance of the overall campaign can then be improvedusing any of a number of optimization techniques including but notlimited to multivariate testing to determine alternative creativesand/or landing pages to try with the non-optimized keywords.

System Architecture

As can be readily appreciated, any of a variety of architectures can beused to implement a system for building a targeted search campaign usinga taxonomy in accordance with embodiments of the invention. In manyembodiments, a campaign creation server system is provided thatcomprises one or more servers configured via software to performprocesses similar to those outlined above. In several embodiments, thecampaign creation server system also includes databases and/orwarehouses to store source data, taxonomies generated using source data,and campaigns built using the taxonomy. A specific architectureconstituting a campaign creation server system in accordance with anembodiment of the invention is illustrated in FIG. 8. Although aspecific architecture is illustrated in FIG. 8, any of a variety ofalternative architectures appropriate to a specific application can alsobe utilized in accordance with embodiments of the invention includinglocally and remotely hosted campaign creation systems and systems thatintegrated with a search engine paid search advertising system.

While the above description contains many specific embodiments of theinvention, these should not be construed as limitations on the scope ofthe invention, but rather as an example of one embodiment thereof.Accordingly, the scope of the invention should be determined not by theembodiments illustrated, but by the appended claims and theirequivalents.

What is claimed:
 1. A method of automatically generating targetinginformation for a plurality of landing pages, comprising: obtainingsource data describing a plurality of landing pages using a campaigncreation server system, where the landing pages include informationdescribing a set of offers; obtaining a list of initial keywords usingthe campaign creation server system, where the keywords in the initiallist of keywords comprise keyword components; building a taxonomy basedon the source data and the initial list of keywords using the campaigncreation server system, where the taxonomy uniquely maps the pluralityof landing pages to categories and attributes and also maps the keywordcomponents to the categories and attributes; specifying target conceptsbased on combinations of keyword components from the initial keywordsusing the campaign creation server system, where each target concept hasa specific set of categories and attributes within the taxonomy definedbased upon the different categories and attributes of the keywordcomponents that are combined to form the target concept; identifying aplurality of offer concepts based on the information describing theplurality of offers using the campaign creation server system, whereeach offer concept is a specific set of categories and attributes withinthe taxonomy identified using the information describing the offer inthe source data describing the plurality of landing pages; matchingtarget concepts to relevant offer concepts within the plurality of offerconcepts based on the specific sets of categories and attributes withinthe taxonomy of the target and offer concepts using the campaigncreation server system; and once a specific offer concept is matched asrelevant to a specific target concept, identifying targeting informationfor one or more of the plurality of landing pages based on theattributes within the taxonomy of the specific target concept thatmatches the relevant offer concept for the landing page using thecampaign creation server system.
 2. The method of claim 1, wherein thecategories are arranged as a hierarchy of categories with associatedattributes.
 3. The method of claim 1, wherein the taxonomy supportsrecursive definition of a first category as an attribute of a secondcategory.
 4. The method of claim 1, wherein: each category is identifiedusing a category name; and each attribute is specified as a key valuepair.
 5. The method of claim 4, wherein the taxonomy includes a set ofcategory names and attribute values that describe products that are thesubject of offers within the plurality of landing pages.
 6. The methodof claim 5, wherein each product described within a landing page isidentified using a unique identifier that is derived from at least oneattribute value that describes the product.
 7. The method of claim 4,wherein matching target concepts to relevant offer concepts comprises:identifying concepts representing unique combinations of category namesand attribute values within the taxonomy using the campaign creationserver system; and predicting keywords that are relevant to theidentified concepts using the campaign creation server system.
 8. Themethod of claim 7, wherein: identifying concepts representing uniquecombinations of category names and attribute values comprises:identifying surface concepts using unique combinations of category namesand attribute values; and identifying deep concepts to which at leastone surface concept is subordinate; and predicting keywords that arerelevant to the identified concepts comprises predicting keywords thatare relevant to the identified deep concepts.
 9. The method of claim 8,further comprising inferring additional attributes of a surface conceptusing an inference engine.
 10. The method of claim 7, wherein predictingkeywords that are relevant to the identified concepts further comprisesgenerating a set of keywords using keyword components within thetaxonomy that are associated with the category name and attribute valuesthat form the basis of the concept.
 11. The method of claim 10, whereingenerating a set of keywords using keyword components within thetaxonomy that are associated with the category name and attribute valuesthat form the basis of the concept further comprises generating keywordsusing the keyword components based upon grammar patterns.
 12. The methodof claim 11, wherein the grammar patterns are associated with specificcategory names within the taxonomy.
 13. The method of claim 12, whereinat least one of the grammar patterns include a grammar component and asemantic component.
 14. The method of claim 13, wherein at least onekeyword is generated by combining the semantic components of a pluralityof grammar patterns in accordance with the grammar components of thegrammar patterns.
 15. The method of claim 10, wherein predictingkeywords that are relevant to the identified concepts further comprises:estimating keyword search volume using search volumes associated withthe grammar patterns and keyword components used to generate thekeyword; and filtering keywords for relevancy to an identified conceptbased upon the estimated keyword search volume.
 16. The method of claim7, wherein matching target concepts to relevant offer concepts furthercomprises identifying at least one target based on a keyword predictedto be relevant to a target concept.
 17. The method of claim 16, whereinmatching target concepts to relevant offer concepts further comprisesidentifying at least one targeting criteria for the at least one target,where the at least one targeting criteria is selected from the groupconsisting of keyword match type, intent, geographic location, profileinformation, language, and information derived from a cookie containedwithin a browser application from which the search query originated. 18.The method of claim 17, wherein matching target concepts to relevantoffer concepts further comprises selecting an offer concept for aspecific target based upon the estimated performance of the offerconcept with respect to search queries targeted by the specific target.19. The method of claim 1, further comprising: automatically generatingone or more anticipated creatives based on the specific sets ofcategories and attributes of the relevant offer concepts using thecampaign creation server system; automatically generating adgroupscomprising at least one of the one or more anticipated creativestargeted towards the landing pages identified in the targetinginformation using the campaign creation server system; and deploying theadgroups to a search engine provider using the campaign creation serversystem, where the search engine provider is configured to present one ormore of the anticipated creatives from the uploaded adgroups in responseto one or more search keywords received using the search engineprovider.
 20. The method of claim 1, wherein specifying target conceptsfurther comprises: generating additional keywords based on the keywordcomponents and grammar rules using the campaign creation server system;and utilizing the additional keywords when specifying the targetconcepts using the campaign creation server system.
 21. The method ofclaim 20, wherein the generated additional keywords are not included inthe list of initial keywords.
 22. A method of automatically generatingadgroups for use in a paid search advertising campaign to directtargeted users to a plurality of landing pages, comprising: obtainingsource data describing a plurality of landing pages using a campaigncreation server system, where the landing pages include informationdescribing a set of offers; obtaining a list of initial keywords usingthe campaign creation server system, where the keywords in the initiallist of keywords comprise keyword components; building a taxonomy basedon the source data and the initial list of keywords using the campaigncreation server system, where the taxonomy uniquely maps the pluralityof landing pages to categories and attributes and maps the keywordcomponents to the categories and attributes; specifying target conceptsbased on combinations of keyword components from the initial keywordsusing the campaign creation server system, where each target concept hasa specific set of categories and attributes within the taxonomy definedbased upon the different categories and attributes of the keywordcomponents that are combined to form the target concept; identifying aplurality of offer concepts based on the information describing theplurality of offers using the campaign creation server system, whereeach offer concept is a specific set of categories and attributes withinthe taxonomy identified using the information describing the offer inthe source data describing the plurality of landing pages; matchingtarget concepts to relevant offer concepts within the plurality of offerconcepts based on the specific sets of categories and attributes withinthe taxonomy of the target and offer concepts using the campaigncreation server system by: identifying at least one surface conceptwithin the taxonomy based on the target concepts and the relevant offerconcepts using the campaign creation server system, where a surfaceconcept in the at least one surface concept comprises keywords andattributes contained in at least one of the target concepts and at leastone of the plurality of offer concepts; identifying at least one deepconcept to which at least one surface concept is subordinate within thetaxonomy using the campaign creation server system, where a deep conceptcomprises all of the category name and attribute values that can be usedto describe the at least one target concept and the at least one offerconcept; and predicting keywords that are relevant to the identifieddeep concepts, where the predicted keywords map the at least one targetconcept to the at least one offer concept; once a specific offer conceptis matched as relevant to a specific target concept, identifying one ormore of the plurality of landing pages that are relevant to the specifictarget concept based on the attributes within the taxonomy of thespecific relevant offer concept using the campaign creation serversystem; automatically generating one or more anticipated creatives basedon the specific sets of categories and attributes of the relevant offerconcepts using the campaign creation server system; automaticallygenerating adgroups comprising: at least one of the one or moreanticipated creatives targeted toward at least one keyword identifiedbased on the specific target concept matched to the specific relevantoffer concept used to generate the anticipated creative; and at leastone of the landing pages identified as relevant to the specific targetconcept based on the attributes in the taxonomy of the specific relevantoffer concept; and deploying the adgroups to a search engine providerusing the campaign creation server system, where the search engineprovider is configured to present one or more of the anticipatedcreatives from the uploaded adgroups in response to one or more searchkeywords received using the search engine provider.
 23. The method ofclaim 19, wherein automatically generating one or more anticipatedcreatives based on the specific sets of categories and attributes of therelevant offer concepts further utilizes at least one of the keywordcomponents.
 24. A targeting information creation server system,comprising: a server and storage; wherein the server is configured to:obtain source data describing a plurality of landing pages, where thelanding pages include information describing a set of offers; obtain alist of initial keywords, where the keywords in the initial list ofkeywords comprise keyword components; build a taxonomy based on thesource data and the initial list of keywords, where the taxonomyuniquely maps the plurality of landing pages to categories andattributes and also maps the keyword components to the categories andattributes; specify target concepts based on combinations of keywordcomponents from the initial keywords, where each target concept has aspecific set of categories and attributes within the taxonomy definedbased upon the different categories and attributes of the keywordcomponents that are combined to form the target concept; identify aplurality of offer concepts based on the information describing theplurality of offers, where each offer concept is a specific set ofcategories and attributes within the taxonomy identified using theinformation describing the offer in the source data describing theplurality of landing pages; match target concepts to relevant offerconcepts within the plurality of offer concepts based on the specificsets of categories and attributes within the taxonomy of the target andoffer concepts; and once a specific offer concept is matched as relevantto a specific target concept, identifying targeting information for oneor more of the plurality of landing pages based on the attributes withinthe taxonomy of the specific target concept that matches the relevantoffer concept for the landing page.